English: Model collapse is a phenomenon in machine learning whereby generative models trained on their own outputs will exhibit increasing test error. However, previous works demonstrating this phenomenon assumed that training data are replaced at every model-fitting iteration; if training data instead accumulate across model-fitting iterations, model collapse does not occur.